In order to encode an image, it is often necessary to reduce spatial redundancies. To this aim, in typical image coding methods, the image is divided into non-overlapping blocks of N×N pixels and each block is then transformed into a transformed block of coefficients. These coding methods decorrelate the image pixels so that the redundancy can be reduced more efficiently in the transform domain. In this respect, the energy compaction property of the transform is important. Among the various transforms commonly used, the Discrete Cosine Transform (DCT) is widely used for its superior energy compaction property. The transformed block represents a set of coefficients with increasing spatial frequencies. The coefficient in the top left (0,0) position of the transformed block is known as the DC coefficient, and it represents the average value of the N×N block. The other (N2-1) coefficients are known as AC coefficients, and they represent the high-frequency details. The dimension N×N can be 16×16, 8×8 or 4×4 according to different applications. In order to reduce the number of bits required to encode the image, typical image coding methods quantize the coefficients of the transformed blocks with a quantizing step. Quantization is the process of reducing the number of possible values of a quantity, thereby reducing the number of bits needed to represent it. The choice of the quantizing step is decisive to insure a high quality of the decoded image.